Cartilage markers and their association with cartilage loss on magnetic resonance imaging in knee osteoarthritis: the Boston Osteoarthritis Knee Study
Bibliographic record
Abstract
We used data from a longitudinal observation study to determine whether markers of cartilage turnover could serve as predictors of cartilage loss on magnetic resonance imaging (MRI). We conducted a study of data from the Boston Osteoarthritis of the Knee Study (BOKS), a completed natural history study of knee osteoarthritis (OA). All subjects in the study met American College of Rheumatology criteria for knee OA. Baseline and follow-up knee magnetic resonance images were scored for cartilage loss by means of the WORMS (Whole Organ Magnetic Resonance Imaging Score) semiquantitative grading scheme. Within the BOKS population, 80 subjects who experienced cartilage loss and 80 subjects who did not were selected for the purposes of this nested case control study. We assessed the baseline levels of cartilage degradation and synthesis products by means of assays for type I and II cleavage by collagenases (Col2:3/4C(short) or C1,2C), type II cleavage only with Col2:3/4C(longmono) (C2C), type II synthesis (C-propeptide), the C-telopeptide of type II (Col2CTx), aggrecan 846 epitope, and cartilage oligomeric matrix protein (COMP). We performed a logistic regression to examine the relation of levels of each biomarker to the risk of cartilage loss in any knee. All analyses were adjusted for gender, age, and body mass index (BMI); results stratified by gender gave similar results. One hundred thirty-seven patients with symptomatic knee OA were assessed. At baseline, the mean (standard deviation) age was 67 (9) years and 54% were male. Seventy-six percent of the subjects had radiographic tibiofemoral OA (Kellgren & Lawrence grade of greater than or equal to 2) and the remainder had patellofemoral OA. With the exception of COMP, none of the other biomarkers was a statistically significant predictor of cartilage loss. For a 1-unit increase in COMP, the odds of cartilage loss increased 6.09 times (95% confidence interval [CI] 1.34 to 27.67). After the analysis of COMP was adjusted for age, gender, and BMI, the risk for cartilage loss was 6.35 (95% CI 1.36 to 29.65). Among subjects with symptomatic knee OA, a single measurement of increased COMP predicted subsequent cartilage loss on MRI. The other biochemical markers of cartilage synthesis and degradation do not facilitate prediction of cartilage loss. With the exception of COMP, if changes in cartilage turnover in patients with symptomatic knee OA are associated with cartilage loss, they do not appear to affect systemic biomarker levels.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".